Welcome

Summary

About this Document

This document is an interactive dashboard viewable from most modern internet browsers. The dashboard is a validation and diagnostics tool for Activity Based Models. Users can compare model performance against a household survey as part of a validation exercise or compare two model runs for sensitivity testing. All of the data, charts, and maps viewable in this dashboard are embedded directly into the HTML file. An internet connection is necessary for the best user experience, but is not required.

Users may navigate to different areas of the dashboard using the navigation bar at the top of the page, and may interact directly with most tables, charts, and maps.

This document is best viewed using the most recent versions of the following web browsers:

Note: Mozilla Firefox does not correctly render the images in this HTML file.

Summary

Modeling Region

Overview

Base Highlights

Survey

Base Population

9,083,501

Base Households

3,621,569

Base Tours

13,477,615

Base Trips

36,754,386

Base Stops

11,562,395

Base VMT

169,359,561

Build Highlights

ASim

Build Population

9,383,855

Build Households

3,621,569

Build Tours

13,005,620

Build Trips

36,323,981

Build Stops

10,312,741

Build VMT

161,184,275

Chart Column 1

Person Type Distribution

Household Size Distribution

Base Highlights2

Survey

Tours per Person

Trips per Person

Stops per Person

Trips per Household

Build Highlights2

ASim

Tours per Person

Trips per Person

Stops per Person

Trips per Household

Long Term Models

Chart Column 1

Auto Ownership

Percentage Working From Home

Chart Column 2

Mandatory TLFD

Flows & Tour Lengths

Chart Column 1

County - County Flow of Workers
2011 - 2015 ACS Commuting Flows
X Cook DeKalb DuPage Grundy Kane Kendall Lake..IL McHenry Will Lake..IN LaPorte Porter Total
Cook 1,985,207 790 141,403 640 19,606 630 64,445 4,860 42,670 15,887 286 683 2,277,107
DeKalb 2,056 28,702 3,197 53 7,676 1,926 305 623 488 38 0 3 45,067
DuPage 138,947 578 253,017 193 19,186 1,595 6,966 755 13,984 730 0 61 436,012
Grundy 1,882 5 1,066 9,943 238 378 2 0 7,495 109 0 0 21,118
Kane 44,221 1,979 46,216 216 119,701 5,099 3,687 6,645 3,972 194 124 0 232,054
Kendall 5,865 1,491 15,642 752 11,372 15,468 229 85 6,420 13 0 0 57,337
Porter 5,149 0 221 33 2 0 109 26 308 22,431 3,843 39,658 71,780
LaPorte 1,022 0 35 0 2 0 6 0 58 2,528 30,005 4,857 38,513
McHenry 32,627 238 5,721 0 12,132 27 20,284 67,762 310 94 0 4 139,199
Lake, IL 77,823 75 7,299 0 1,974 10 219,268 7,538 543 168 0 0 314,698
Will 89,460 108 56,335 3,583 5,632 2,605 1,166 327 143,426 2,740 9 212 305,603
Lake, IN 42,490 0 2,101 73 130 11 332 23 3,772 138,217 1,480 10,183 198,812
Total 2,426,749 33,966 532,253 15,486 197,651 27,749 316,799 88,644 223,446 183,149 35,747 55,661 4,137,300

Average Mandatory Tour Lengths
Survey
Home District Work University School
Cook 10.67 9.33 3.53
DeKalb 10.30 5.10 6.23
DuPage 13.00 12.96 3.69
Grundy 17.28 25.56 4.23
Kane 13.90 22.06 5.57
Kendall 15.62 24.83 4.67
Lake, IL 13.74 18.08 4.77
Lake, IN 17.40 16.73 5.36
LaPorte 16.92 19.63 6.78
McHenry 16.49 16.39 4.75
Porter 17.54 15.25 4.56
Will 16.39 21.43 4.89
Total 12.47 11.83 4.12

Chart Column 1

County - County Flow of Workers
ASim
X Cook DeKalb DuPage Grundy Kane Kendall Lake..IL Lake..IN LaPorte McHenry Porter Will Total
Cook 1,845,541 395 167,005 315 18,271 623 66,296 33,152 391 5,074 1,673 39,146 2,182,296
DeKalb 4,310 15,399 6,102 69 9,184 1,235 757 4 0 1,108 0 732 43,115
DuPage 150,009 761 213,307 322 24,681 1,905 7,073 579 18 1,199 29 17,521 418,440
Grundy 2,419 54 2,401 6,848 891 532 36 156 0 40 14 6,338 21,552
Kane 53,802 3,270 60,582 308 80,015 4,650 8,098 54 4 8,040 4 7,098 229,104
Kendall 8,167 858 16,717 844 13,009 7,928 311 109 0 290 11 8,800 58,097
Lake, IL 94,244 163 9,579 0 4,205 51 160,457 109 4 11,158 11 232 295,444
Lake, IN 67,372 0 1,901 47 58 0 203 94,443 1,199 7 10,296 5,954 182,864
LaPorte 2,093 0 47 0 0 0 7 2,756 27,803 0 6,830 163 39,732
McHenry 34,843 916 7,946 11 15,924 101 28,219 18 0 40,609 4 286 140,231
Porter 9,358 0 203 11 0 0 43 18,379 4,321 4 34,666 706 67,886
Walworth 2,206 76 355 0 833 7 3,556 0 0 4,704 0 25 44,031
Winnebago 1,354 761 623 0 1,184 18 739 0 0 2,101 0 4 110,284

Average Mandatory Tour Lengths
ASim
Home District Work University School
Cook 10.90 1.45 4.38
DeKalb 25.26 2.96 7.90
DuPage 14.03 1.97 5.79
Grundy 28.04 3.69 8.16
Kane 18.04 2.48 6.26
Kendall 23.03 3.63 6.73
Lake, IL 16.90 2.45 5.97
Lake, IN 18.31 3.56 6.53
LaPorte 16.75 3.59 6.55
McHenry 21.87 3.22 7.07
Porter 18.28 3.72 7.05
Will 20.03 2.99 6.41
Total 14.46 2.11 5.39

Work Tours

Chart Column 1

Tour Production Comparison

County ACS ASim
Cook 4,554,214 4,360,177
DeKalb 90,134 82,014
DuPage 872,024 835,844
Grundy 42,236 41,282
Kane 464,108 455,028
Kendall 114,674 115,141
Porter 143,560 135,577
LaPorte 77,026 79,432
McHenry 278,398 269,108
Lake, IL 629,396 575,656
Will 611,206 593,398
Lake, IN 397,624 364,344

Chart Column 1

Tour Attraction Comparison

County ACS ASim
Cook 4,853,498 4,743,303
DeKalb 67,932 47,874
DuPage 1,064,506 1,115,049
Grundy 30,972 25,308
Kane 395,302 362,501
Kendall 55,498 44,491
Porter 633,598 108,252
LaPorte 177,288 67,676
McHenry 446,892 148,339
Lake, IL 366,298 577,253
Will 71,494 399,640
Lake, IN 111,322 314,895

Zero Auto Households

Summary

Zero Auto Households Census vs Model

Tour Summaries

Chart Column 1

Daily Activity Pattern

Percentage of Households with a Joint Tour

Mandatory Tour Frequency

Chart Column 1

Total Tour Rate (only active Persons)

Persons by Individual Non-Mandatory Tours

Joint Tours

Chart Column 1

Joint Tour Frequency

Joint Tour Composition

Chart Column 1

Joint Tours By Number of Household Members

Joint Tours by Household Size

Party Size Distribution by Joint Tour Composition

Destination

Chart Column 1

Non-Mandatory Tour Length Distribution

Average Non-Mandatory Tour Lengths (Miles)

Purpose Survey ASim
Escorting 4.46 4.16
Indi-Maintenance 7.26 6.48
Indi-Discretionary 6.07 7.24
Joint-Maintenance 6.81 6.78
Joint-Discretionary 7.11 7.62
At-Work 5.00 2.76
Total 6.22 5.90

TOD

Chart Column 1

Tour Departure-Arrival Profile

Tour Aggregate Departure-Arrival Profile

Tour Mode

Chart Column 1

Tour Mode Choice


Tour Mode Choice

Results of Tour Mode Choice Models, which selects a primary mode for each tour.

Distribution of tours by tour mode and the ratio of autos to drivers in the household.

Chart Column 2

Chart Column 3

Stop Frequency

Chart Column 1

Stop Frequency - Directional

Chart Column 1

Stop Frequency - Total

Stop Purpose by Tour Purpose

Location

Chart Column 1

Stop Location - Out of Direction Distance

Chart Column 1

Average Out of Direction Distance (Miles)

_______________________________________________________
Tour_Purpose Survey ASim
Work 4 3.43211892833935
University 3 4.60947567955227
School 5 4.60083069620942
Escorting 4 3.70379644081787
Indi-Maintenance 4 3.75442004093355
Indi-Discretionary 5 3.6555250512802
Joint-Maintenance 4 3.76836469751957
Joint-Discretionary 4 3.77877305414742
At-Work 3 2.2741173270391
Total 4 3.73654069714256

TOD

Chart Column 1

Stop & Trip Departure

Aggregate Stop & Trip Departure

Trip Mode

Chart Column 1

Trip Mode Choice

The results of the Trip Mode Choice Model, which predicts the mode of each trip on the tour.

Distribution of trips by trip mode and tour mode, which constrains the availability of each trip mode and influences the utility of each available trip mode.

Trip Mode Choice

Chart Column 2

Count vs Volume: All Day

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: NT

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: EA

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: AM

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: MM

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: MD

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: AF

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: PM

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

Count vs Volume: EV

Chart Column 2

RMSE Statistics

Assigned VMT Statistics

---
title: "`r paste(BASE_SCENARIO_NAME, 'vs.', BUILD_SCENARIO_NAME, 'Calibration Summary')`"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: spacelab
    social: menu
    source_code: embed
runtime: shiny
---

```{r Setup}
opts_knit$set(root.dir = SYSTEM_APP_PATH)
knit_hooks$set(optipng = hook_optipng)
```

```{r setpar}
knitr::opts_knit$set(global.par = TRUE)
```


```{r ggplot_Theme}
theme_db <- theme_bw() + theme(plot.margin = unit(c(10,10,20,10),"pt"))
```

```{r Helper_Functions}
compare_bar_plotter <- function(base, build, base_name, build_name, xvar, yvar,
                        xlabel = xvar, ylabel = yvar, position = "dodge",
                        xrotate = FALSE, yrotate = FALSE, coord_flip = FALSE,
                        title = "", left_offset = 0, bottom_offset = 0){

  base$grp <- base_name
  build$grp <- build_name
  colnames(build) <- colnames(base)

  df <- rbind(base, build)

  p <- ggplot(df, aes_string(x = xvar, y = yvar)) +
    geom_bar(stat = "identity", aes(fill = grp), position = position) +
    xlab(xlabel) + ylab(ylabel) +
    labs(fill = "") +
    ggtitle(title) +
    theme(axis.text.x=element_text(angle=50, size=1, vjust=0.5)) +
    theme(axis.text.y=element_text(angle=50, size=1, vjust=0.5)) +
    theme_bw()

  if (xrotate) {
    p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))
  }
  if (yrotate) {
    p <- p + theme(axis.text.y = element_text(angle = 45, hjust = 1))
  }
  if (coord_flip) {
    p <- p + coord_flip()
  }


  p <- plotly_build(p)
  p$layout$margin$l <- p$layout$margin$l+left_offset
  p$layout$margin$b <- p$layout$margin$b+bottom_offset
  return(p)

}

# This function combines two dataframes and returns a data frame with standard field names
# The field names in the two dataframes should be same and should be same as the variable
# names passed to the function
# input parameter - dataframe1, dataframe2, x variable, list of y variables
# renames x and y variables in standard form - xvar, (yvar1, yvar2),...
# Y variables are named in pairs - (yvar1, yvar2), (yvar3, yvar4), ....
# yvar1, yvar3, .. correspond to first dataframe and yvar2, yvar4, .. correspond to second dataframe
# computes proportions for each  y variable variable
get_standardDF <- function(data_df1, data_df2, x, y, grp = "", shared = F){

  #data_df1=base_df
  #data_df2=build_df
  #x="id"
  #y = c("freq_out", "freq_inb")
  #grp = "purpose"
  #shared = T
  #
  # create ID variable to join base and build data
  if(!shared){
    ev1 <- paste("data_df1$id_var <- data_df1$", x, sep = "")
    ev2 <- paste("data_df2$id_var <- data_df2$", x, sep = "")
    eval(parse(text = ev1))
    eval(parse(text = ev2))
  }else{
    if(grp==""){
      stop("group variable not specified")
    }else{
      ev1 <- paste("data_df1$id_var <- paste(data_df1$", grp, ", data_df1$", x, ', sep = "")', sep = "")
      ev2 <- paste("data_df2$id_var <- paste(data_df2$", grp, ", data_df2$", x, ', sep = "")', sep = "")
      eval(parse(text = ev1))
      eval(parse(text = ev2))
    }
  }

  data_df <- data_df1

  # rename variables to standard names
  names(data_df)[names(data_df) == x] <- 'xvar'
  if(shared){
    if(grp==""){
      stop("group variable not specified")
    }else{
      names(data_df)[names(data_df) == grp] <- 'grp_var'
    }
  }

  for(i in seq(from=1, to=length(y))){
    start_pos <- i*2-1
    yvar1 <- paste('yvar', start_pos, sep = "")
    yvar2 <- paste('yvar', start_pos+1, sep = "")
    names(data_df)[names(data_df) == y[[i]]] <- paste('yvar', start_pos, sep = "")
    eval_expr <- paste("data_df$", yvar2, " <- data_df2$", y[[i]], "[match(data_df$id_var, data_df2$id_var)]", sep = "")
    eval(parse(text = eval_expr))
  }
  
  data_df[is.na(data_df)] <- 0

  #data_df$grp_var <- as.character(data_df$grp_var)

  # compute proportions for y variables
  for(i in seq(from=1, to=length(y))){
    start_pos <- i*2-1
    prop_var1 <- paste('prop', start_pos, sep = "")
    y_var1    <- paste('yvar', start_pos, sep = "")
    prop_var2 <- paste('prop', start_pos+1, sep = "")
    y_var2    <- paste('yvar', start_pos+1, sep = "")
    if(shared){
      if(grp==""){
        stop("group variable not specified")
      }else{
        eval_expr1 <- paste("data_df <- data_df %>% group_by(grp_var) %>% mutate(", prop_var1, " = prop.table(", y_var1, "))", sep = "")
        eval_expr2 <- paste("data_df <- data_df %>% group_by(grp_var) %>% mutate(", prop_var2, " = prop.table(", y_var2, "))", sep = "")
      }
    }else{
      eval_expr1 <- paste("data_df <- data_df %>% mutate(", prop_var1, " = prop.table(", y_var1, "))", sep = "")
      eval_expr2 <- paste("data_df <- data_df %>% mutate(", prop_var2, " = prop.table(", y_var2, "))", sep = "")
    }

    eval(parse(text = eval_expr1))
    eval(parse(text = eval_expr2))
  }

  # set all NAs to zero
  # data_df[is.na(data_df)] <- 0

  if(!shared){
    return(data_df)
  }else{
    data_sd <- SharedData$new(data_df, ~grp_var)
    return(data_sd)
  }
}

# This function returns a SharedData object for creating comparison density plots
# input parameter - dataframe1, dataframe2, x variable, list of y variables,
# grouping variable, names of each run
# The function expects same field names across both dataframes
# renames x and y variables in standard form - xvar, yvar1, yvar2,...
# computes proportions for each  y variable variable for each group and run
# combines two dataframe and adds a run identifier
get_sharedData <- function(data_df1, data_df2, run1_name = 'base', run2_name = 'build',
                           x, y, grp){

  # rename variables to standard names
  names(data_df1)[names(data_df1) == x] <- 'xvar'
  names(data_df1)[names(data_df1) == grp] <- 'grp_var'
  for(i in 1:length(y)){
    names(data_df1)[names(data_df1) == y[[i]]] <- paste('yvar', i, sep = "")
  }

  names(data_df2)[names(data_df2) == x] <- 'xvar'
  names(data_df2)[names(data_df2) == grp] <- 'grp_var'
  for(i in 1:length(y)){
    names(data_df2)[names(data_df2) == y[[i]]] <- paste('yvar', i, sep = "")
  }

  # compute proportions for y variables
  data_df1 <- group_by(data_df1, grp_var)
  for(i in 1:length(y)){
    prop_var <- paste('prop', i, sep = "")
    y_var    <- paste('yvar', i, sep = "")
    eval_expr <- paste("data_df1 <- data_df1 %>% mutate(", prop_var, " = prop.table(", y_var, "))", sep = "")
    eval(parse(text = eval_expr))
  }

  data_df2 <- group_by(data_df2, grp_var)
  for(i in 1:length(y)){
    prop_var <- paste('prop', i, sep = "")
    y_var    <- paste('yvar', i, sep = "")
    eval_expr <- paste("data_df2 <- data_df2 %>% mutate(", prop_var, " = prop.table(", y_var, "))", sep = "")
    eval(parse(text = eval_expr))
  }

  # add run identifiers
  data_df1$run <- run1_name
  data_df2$run <- run2_name

  # combine dataframes
  data_df <- rbind(data_df1, data_df2)

  # set all NAs to zero
  data_df[is.na(data_df)] <- 0

  data_sd <- SharedData$new(data_df, ~grp_var)
  return(data_sd)
}

# This function returns bar plot for a given X-Y data frame
# The function expects the data frame columns to be named as
# xvar, yvar1, yvar2...
# function plots only two series at a time
# which two y series to plot is determined by the index variable
# index==1 :- yvar1, yvar2, index==2 :- yvar,3,4 and so on
# names of series to be plotted should also be passed as a list argument
# number of elements in names list determines the number of series to be added
plotly_bar_plotter <- function(data, type = 'bar', xlabel = "", ylabel = "", percent = FALSE,
                               title = "", height = 0, width = 0, ynames = c(""), left_offset = 0,
                               bottom_offset = 0, tickformat = "", hoverformat = "", tickangle = 0, index = 1, tickvals = c(), ticktext = c()){
  #initial setup
  start_pos <- 2*index - 1
  exp_tickvals <- ifelse(length(tickvals)>0, ', tickvals = tickvals', "")
  exp_ticktext <- ifelse(length(ticktext)>0, ', ticktext = ticktext', "")

  #generate plot
  if(!percent){
    ylab <- ifelse(ylabel=="", "Percent", ylabel)
    hformat <- ifelse(hoverformat=="", '.1f', hoverformat)
    eval_expr <- paste("p <- plot_ly(data, x = ~xvar, y = ~yvar", start_pos, ", type = type, name = ynames[[1]]) %>% ",
                       "add_trace(y = ~yvar", start_pos+1, ", name = ynames[[2]]) %>% ",
                       "layout(yaxis = list(hoverformat = hformat, title = ylab, tickformat = tickformat), xaxis = list(title = xlabel, tickangle = tickangle", exp_tickvals, exp_ticktext, "), barmode = 'group')", sep = "")
    eval(parse(text = eval_expr))
  }else{
    ylab <- ifelse(ylabel=="", "Frequency", ylabel)
    hformat <- ifelse(hoverformat=="", '.1%', hoverformat)
    eval_expr <- paste("p <- plot_ly(data, x = ~xvar, y = ~prop", start_pos, ", type = type, name = ynames[[1]]) %>% ",
                       "add_trace(y = ~prop", start_pos+1, ", name = ynames[[2]]) %>% ",
                       "layout(yaxis = list(hoverformat = hformat, title = ylab, tickformat = '.0%'), xaxis = list(title = xlabel, tickangle = tickangle", exp_tickvals, exp_ticktext,"), barmode = 'group')", sep = "")
    eval(parse(text = eval_expr))
  }

  p$x$layout$height <- height
  p$x$layout$width <- width
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  return(p)
}

# This function returns a spline plot with fill for a gievn X-Y dataframe
# The function expects the data frame columns to be named as
# x = ~xvar, y = (~yvar1 or prop1),  (~yvar2 or prop2) adn so on (Frequency or Percent),
# which y to use is determined by index parameter (one, two or three)
# and variable differentiating runs as ~run
# The function currebtly plots only one Y variables for each run
plotly_density_plotter <- function(data_df, index = "one", colors=c("orange", "steelblue"), fill = 'tozeroy',
                                   title = "", xlabel = "", ylabel = "", percent = T, alpha = 0.7, tickvals, ticktext, tickangle = 0,
                                   height=400, left_offset = 0, bottom_offset = 0, shape = 'spline', legend = T){
  ##standardize data frame
  #names(data_df)[names(data_df)==xvar]     <- 'xvar'
  #names(data_df)[names(data_df)==yvar]     <- 'yvar1'
  #names(data_df)[names(data_df)==prop_var] <- 'prop1'
  #names(data_df)[names(data_df)==grp]      <- 'run'

  # prepare plot using standardized dataframe
  if(percent){
    ylab <- ifelse(ylabel=="", "Percent", ylabel)

    p <- switch(index,
                "one" = plot_ly(data=data_df,x = ~xvar, y = ~prop1, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = ".0%"), showlegend = legend),
                "two" = plot_ly(data=data_df,x = ~xvar, y = ~prop2, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = ".0%"), showlegend = legend),
                "three" = plot_ly(data=data_df,x = ~xvar, y = ~prop3, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab, tickformat = ".0%"), showlegend = legend)
                )

  }else{
    ylab <- ifelse(ylabel=="", "Frequency", ylabel)

    p <- switch(index,
                "one" = plot_ly(data=data_df,x = ~xvar, y = ~yvar1, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend),
                "two" = plot_ly(data=data_df,x = ~xvar, y = ~yvar2, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend),
                "three" = plot_ly(data=data_df,x = ~xvar, y = ~yvar3, colors=c("steelblue", "orange"), color = ~run, fill=fill) %>%
                  add_lines(name=~run,alpha=alpha, line = list(shape = shape)) %>%
                  layout(title = "",xaxis = list(title=xlabel, tickvals = tickvals, ticktext = ticktext, tickangle = tickangle), yaxis = list(title=ylab), showlegend = legend)
                )

    #p <- plot_ly(data=data_df,x = ~xvar, y = ~yvar1, colors=c("steelblue", "orange"), color = ~run, height=400, fill=fill) %>%
    #add_lines(name="",alpha=alpha, line = list(shape = shape)) %>%
    #layout(title = "",xaxis = list(title=xlabel), yaxis = list(title=ylab))
  }

  p$x$layout$height <- height
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  return(p)
}

# This function returns a pie chart
# Input is a 2 column data frame with a label variable and a value variable
plotly_pie_chart <- function(data, label_var, value_var, title = "",
                               height = 0, width = 0, left_offset = 0,bottom_offset = 0, top_offset = 0, shared = F){

  colors <- c('rgb(211,94,96)', 'rgb(128,133,133)', 'rgb(144,103,167)', 'rgb(171,104,87)', 'rgb(114,147,203)')

  if(!shared){
    names(data)[names(data)==label_var] <- 'label_var'
    names(data)[names(data)==value_var] <- 'value_var'

    p <- plot_ly(data, labels = ~label_var, values = ~value_var, type = 'pie',
          textposition = 'outside',
          textinfo = 'label+percent',
          insidetextfont = list(color = '#FFFFFF'),
          marker = list(colors = colors,
                        line = list(color = '#FFFFFF', width = 2)),
          showlegend = FALSE,
          sort = FALSE) %>%
    layout(title = title,
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
  }else{
    eval_expr <- paste("p <- plot_ly(data, labels = ~", label_var, ", values = ~", value_var, ", type = 'pie',
          textposition = 'outside',
          textinfo = 'label+percent',
          insidetextfont = list(color = '#FFFFFF'),
          marker = list(colors = colors,
                        line = list(color = '#FFFFFF', width = 2)),
          showlegend = FALSE,
          sort = FALSE) %>%
    layout(title = title,
           xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
           yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))", sep = "")

    eval(parse(text = eval_expr))
  }


  p$x$layout$height <- height
  p$x$layout$width <- width
  p$x$layout$margin$b <- p$x$layout$margin$b + bottom_offset
  p$x$layout$margin$l <- p$x$layout$margin$l + left_offset
  p$x$layout$margin$t <- p$x$layout$margin$t + top_offset
  return(p)
}

lm_eqn <- function(df){
    m <- lm(y ~ x - 1, df);
    eq <- paste("Y = ", format(coef(m)[1], digits = 2), " * X , ", " r2 = ", format(summary(m)$r.squared, digits = 3), sep = "")
    return(eq)
}

```

Welcome
============================================

Summary {data-width=150}
--------------------------------------------

### About this Document

This document is an interactive dashboard viewable from most modern internet browsers. The dashboard is a validation and diagnostics tool for Activity Based Models. Users can compare model performance against a household survey as part of a validation exercise or compare two model runs for sensitivity testing. All of the data, charts, and maps viewable in this dashboard are embedded directly into the HTML file. An internet connection is necessary for the best user experience, but is not required.

Users may navigate to different areas of the dashboard using the navigation bar at the top of the page, and may interact directly with most tables, charts, and maps.

This document is best viewed using the most recent versions of the following web browsers:

* [Google Chrome](https://www.google.com/chrome/browser/desktop/)
* [Microsoft Internet Explorer](https://www.microsoft.com/en-us/download/internet-explorer.aspx)

Note: Mozilla Firefox does not correctly render the images in this HTML file.

Summary {data-width=600}
--------------------------------------------

### Modeling Region
```{r model_region}
# cat("Number of TAZs:", nrow(zone_shp))
# bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
# pal <- colorBin("YlOrRd", domain = zone_shp$HH, bins = bins)

m <- leaflet(data = zone_shp)%>%
 addTiles() %>%
 addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>%
 addLayersControl(
   overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE)
 ) %>%
 addPolygons(weight = 0.5, opacity = 1)
m

# m <- leaflet(data = zone_shp)%>%
#  addTiles() %>%
#  addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>%
#  addLayersControl(
#    overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE)
#  )
# m
#
```


Overview
============================================

Base Highlights {data-width=90}
--------------------------------------------

###

```{r Run_Date1_ValueBox}
valueBox(BASE_SCENARIO_NAME, paste("Sample Rate: ", round(BASE_SAMPLE_RATE*100,2), "%", sep = ""), color = "DarkOrange")
base_pos <- which(base_csv_names=="totals")
base_df <- base_data[[base_pos]]
```

### Base Population
```{r Population1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_population"]/BASE_SAMPLE_RATE), big.mark = ","), "Population", icon = "ion-ios-people")
```

### Base Households
```{r Household1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_households"]/BASE_SAMPLE_RATE), big.mark = ","), "Households", icon = "glyphicon glyphicon-home")
```

### Base Tours
```{r Tours1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_tours"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Tours", icon = "ion-refresh")
```

### Base Trips
```{r Trips1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_trips"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Trips", icon = "ion-loop")
```

### Base Stops
```{r Stops1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_stops"]/BASE_SAMPLE_RATE), big.mark = ","), "Total Stops", icon = "ion-ios-location")
```

### Base VMT
```{r VMT1_ValueBox}
valueBox(prettyNum(round(base_df$value[base_df$name=="total_vmt"]/BASE_SAMPLE_RATE), big.mark = ","), "Total VMT", icon = "ion-android-car")
```



Build Highlights {data-width=90}
--------------------------------------------

###

```{r Run_Date2_ValueBox}
valueBox(BUILD_SCENARIO_NAME, paste("Sample Rate: ", round(BUILD_SAMPLE_RATE*100,2), "%", sep = ""), color = "DarkOrange")
build_pos <- which(build_csv_names=="totals")
build_df <- build_data[[build_pos]]
```

### Build Population
```{r Population2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_population"]/BUILD_SAMPLE_RATE), big.mark = ","), "Population", icon = "ion-ios-people")
```

### Build Households
```{r Household2_ValueBox}
valueBox(prettyNum(format(round(build_df$value[build_df$name=="total_households"]/BUILD_SAMPLE_RATE), scientific=F), big.mark = ","), "Households", icon = "glyphicon glyphicon-home")
```

### Build Tours
```{r Tours2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_tours"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Tours", icon = "ion-refresh")
```

### Build Trips
```{r Trips2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_trips"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Trips", icon = "ion-loop")
```

### Build Stops
```{r Stops2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_stops"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total Stops", icon = "ion-ios-location")
```

### Build VMT
```{r VMT2_ValueBox}
valueBox(prettyNum(round(build_df$value[build_df$name=="total_vmt"]/BUILD_SAMPLE_RATE), big.mark = ","), "Total VMT", icon = "ion-android-car")
```


Chart Column 1 {data-width=275}
--------------------------------------------
### Person Type Distribution
```{r Chart_Person_Type}
base_pos <- which(base_csv_names=="pertypeDistbn")
base_df <- base_data[[base_pos]]
base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)]
base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char)
build_pos <- which(build_csv_names=="pertypeDistbn")
build_df <- build_data[[build_pos]]
build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)]
build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char)

colnames(build_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "PERNAME", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "Person Type", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, bottom_offset = 60, tickangle = -30)
p

```

### Household Size Distribution
```{r Chart_HHSize}
base_pos <- which(base_csv_names=="hhSizeDist")
base_df <- base_data[[base_pos]]

census_df = base_data[[which(base_csv_names=="hhSizeCensus")]]


build_pos <- which(build_csv_names=="hhSizeDist")
build_df <- build_data[[build_pos]]

colnames(census_df) <- colnames(base_df)

std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHSIZE", y = c("freq"))
p <- plotly_bar_plotter(data = std_DF, xlabel = "HH Size", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T)
p

```

Base Highlights2 {data-width=90}
--------------------------------------------

###

```{r Run_Date3_ValueBox}
valueBox(BASE_SCENARIO_NAME, "", color = "DarkOrange")
base_pos <- which(base_csv_names=="totals")
base_df <- base_data[[base_pos]]
```


### Tours per Person
```{r TourRate3_Gauge}
rate <- base_df$value[base_df$name=="total_tours"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Person
```{r TripRate3_Gauge}
rate <- base_df$value[base_df$name=="total_trips"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 5, gaugeSectors(danger = c(0,5), colors = c("Green", "Green", "Green")))
```

### Stops per Person
```{r StopRate3_Gauge}
rate <- base_df$value[base_df$name=="total_stops"]/base_df$value[base_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Household
```{r TRate3_Gauge}
rate <- base_df$value[base_df$name=="total_trips"]/base_df$value[base_df$name=="total_households"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 15, gaugeSectors(danger = c(0,15), colors = c("Green", "Green", "Green")))
```


Build Highlights2 {data-width=90}
--------------------------------------------

###

```{r Run_Date4_ValueBox}
valueBox(BUILD_SCENARIO_NAME, "", color = "DarkOrange")
build_pos <- which(build_csv_names=="totals")
build_df <- build_data[[build_pos]]
```


### Tours per Person
```{r TourRate4_Gauge}
rate <- build_df$value[build_df$name=="total_tours"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger =  c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Person
```{r TripRate4_Gauge}
rate <- build_df$value[build_df$name=="total_trips"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 5, gaugeSectors(danger = c(0,5), colors = c("Green", "Green", "Green")))
```

### Stops per Person
```{r StopRate4_Gauge}
rate <- build_df$value[build_df$name=="total_stops"]/build_df$value[build_df$name=="total_population"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 2, gaugeSectors(danger = c(0,2), colors = c("Green", "Green", "Green")))
```

### Trips per Household
```{r TRate4_Gauge}
rate <- build_df$value[build_df$name=="total_trips"]/build_df$value[build_df$name=="total_households"]
gauge(prettyNum(round(rate, 2), big.mark = ","), min = 0, max = 15, gaugeSectors(danger = c(0,15), colors = c("Green", "Green", "Green")))
```


Long Term Models{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=225}
--------------------------------------------


**Auto Ownership**

Results of household auto ownership model, which predicts number of vehicles per household.

**Mandatory TLFD**

Results of work and school location choice models.

Distribution of workers by distance between home and usual work place, and students by distance between home and school location.

Chart Column 1 {data-width=300}
--------------------------------------------
### Auto Ownership{data-height=265}
```{r Chart_Auto_Ownership}
if(IS_BASE_SURVEY=="Yes"){
  #cat("Census source: ", AO_CENSUS_LONG)
  # [3/31/2020] DH No census data used for SEMCOG
  census_df <- base_data[[which(base_csv_names=="autoOwnershipCensus")]]
  # base_df <- base_data[[which(base_csv_names=="autoOwnership")]]
  base_df <- base_data[[which(base_csv_names=="autoOwnership")]]

}else{
  base_df <- base_data[[which(base_csv_names=="autoOwnership")]]
}

build_df <- build_data[[which(build_csv_names=="autoOwnership")]]
build_df$X = NULL
base_df$X = NULL
#colnames(build_df) <- colnames(census_df)
#colnames(base_df) <- colnames(census_df)

base_df$HDISTRICT = levels(base_df$HDISTRICT)[base_df$HDISTRICT]
build_df$HDISTRICT = levels(build_df$HDISTRICT)[build_df$HDISTRICT]

# Limit to surveyed counties
build_df = build_df[build_df$HDISTRICT %in% base_df$HDISTRICT,]

sd.autoown <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHVEH", y = c("freq"), grp = "HDISTRICT", shared = T)

p <- plotly_bar_plotter(data = sd.autoown, xlabel = "Number of Vehicles", ylabel = "Percent", ynames = c(AO_CENSUS_LONG, BUILD_SCENARIO_NAME), percent = T, height = 225)

bscols(widths=c(3,9),
  list(
    filter_select("ao_cname", "Select County", sd.autoown, ~grp_var, multiple=F)),
    p
  )

#p
```

### {data-height=140}
```{r Gauge_WFH1}


```

### Percentage Working From Home{data-height=250}
```{r Chart_WFH}
base_df <- base_data[[which(base_csv_names=="wfh_summary")]]
base_df$share <- base_df$WFH/base_df$Workers
build_df <- build_data[[which(build_csv_names=="wfh_summary")]]
build_df$share <- build_df$WFH/build_df$Workers
build_df = build_df[which(str_to_upper(build_df$District) %in% str_to_upper(base_df$District)),]
std_DF <- cbind(base_df[,c("District", "share")], build_df[,c("share")])
colnames(std_DF) <- c("xvar", "prop1", "prop2")
p <- plotly_bar_plotter(data = std_DF, xlabel = "District", ylabel = "Percent WFH", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 275, tickangle = -320, bottom_offset = 25)
p

```



Chart Column 2 {data-width=350}
--------------------------------------------


### Mandatory TLFD{data-height=475}
```{r mandatoryTLFD}
base_df1 <- base_data[[which(base_csv_names=="workTLFD")]]
base_df1 <- melt(base_df1, id = c("distbin"))

base_df2 <- base_data[[which(base_csv_names=="univTLFD")]]
base_df2 <- melt(base_df2, id = c("distbin"))

base_df3 <- base_data[[which(base_csv_names=="schlTLFD")]]
base_df3 <- melt(base_df3, id = c("distbin"))

base_df <- cbind(base_df1, base_df2$value, base_df3$value)
colnames(base_df) <- c("distbin","variable","value1","value2","value3")

build_df1 <- build_data[[which(build_csv_names=="workTLFD")]]
build_df1 <- melt(build_df1, id = c("distbin"))

build_df2 <- build_data[[which(build_csv_names=="univTLFD")]]
build_df2 <- melt(build_df2, id = c("distbin"))

build_df3 <- build_data[[which(build_csv_names=="schlTLFD")]]
build_df3 <- melt(build_df3, id = c("distbin"))

build_df <- cbind(build_df1, build_df2$value, build_df3$value)
colnames(build_df) <- c("distbin","variable","value1","value2","value3")

sd.purpose <- get_sharedData(data_df1 = build_df, data_df2 = base_df, run1_name = BUILD_SCENARIO_NAME,
                             run2_name = BASE_SCENARIO_NAME, x = "distbin", y = c("value1", "value2", "value3"), grp = "variable")

p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Miles to Work", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Miles to University", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)
p3 <- plotly_density_plotter(sd.purpose, index = "three", xlabel = "Miles to School", percent = T, tickvals = seq(1,50,5), ticktext = seq(0,50,5), height = 240)

bscols(widths=c(12),
  list(filter_select("Purpose_County", "Select District", sd.purpose, ~grp_var,multiple=F),
  p1,
  p2,
  p3)
  )

```


Flows & Tour Lengths{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=135}
--------------------------------------------

**County-County Flow of Workers**

Crosstab of workers by home county and usual work place county.

Note: Districts can be Tract, County, District etc.

**Average Tour Lengths**

Average tour length to workplace by District of residence


Chart Column 1
--------------------------------------------

### {data-height=300}
```{r Table1_CountyFlows, eval  = TRUE}
cat("County - County Flow of Workers")

base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]


base_df[,!colnames(base_df) %in% c("X")] <- base_df[,!colnames(base_df) %in% c("X")]/BASE_SAMPLE_RATE
t1 <- kable(base_df, format = 'html', caption = DISTRICT_FLOW_CENSUS, digits = 0, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10)
t1

```

### {data-height=280}
```{r Table1_MandTripLengths}
cat("Average Mandatory Tour Lengths")

base_df <- base_data[[which(base_csv_names=="mandTripLengths")]]
df <- base_df
colnames(df) <- c("Home District", "Work","University","School")

eval_expr <- paste("t1 <- kable(df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') %>%
  add_header_above(c(' ' = 1, '", BASE_SCENARIO_NAME, "' = 3))", sep = "")
eval(parse(text = eval_expr))
t1
```


Chart Column 1
-------------------------------------------- 

### {data-height=300}
```{r Table2_CountyFlows, eval = TRUE}
cat("County - County Flow of Workers")

base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]

build_df = build_df[levels(build_df$X) %in% levels(base_df$X),colnames(build_df) %in% colnames(base_df)]


build_df[,!colnames(build_df) %in% c("X")] <- build_df[,!colnames(build_df) %in% c("X")]/BUILD_SAMPLE_RATE
t2 <- kable(build_df, format = 'html', caption = BUILD_SCENARIO_NAME, digits = 0, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10)
t2
```

### {data-height=280}
```{r Table2_MandTripLengths}
cat("Average Mandatory Tour Lengths")

base_df <- base_data[[which(base_csv_names=="mandTripLengths")]]

build_df <- build_data[[which(build_csv_names=="mandTourLengths")]]
df <- build_df[levels(build_df$District)[build_df$District] %in% levels(base_df$District)[base_df$District],]
colnames(df) <- c("Home District", "Work","University","School")

eval_expr <- paste("t2 <- kable(df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') %>%
  add_header_above(c(' ' = 1, '", BUILD_SCENARIO_NAME, "' = 3))", sep = "")
eval(parse(text = eval_expr))
t2
```


Work Tours{data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=135}
--------------------------------------------

**Tour Productions by County**

Comparison of work productions, model vs. ACS Journey-To-Work 2011-2015. NOTE: Other ACS data is 2017, this data is NOT adjusted for date, it is adjusted for work-from-home.

Note: Districts can be Tract, County, District etc.

**Tour Attractions by County**

Comparison of work attractions, model vs. ACS Journey-To-Work 2011-2015. NOTE: Other ACS data is 2017, this data is NOT adjusted for date, it is adjusted for work-from-home.

Chart Column 1
--------------------------------------------
### Tour Production Comparison {data-height=300}
```{r aWorkTourProds}

base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]

build_df = build_df[levels(build_df$X)[build_df$X] %in% levels(base_df$X)[base_df$X],colnames(build_df) %in% colnames(base_df)]

wp_df = data.frame(County = base_df[,"X"], ACSWorkProds = rowSums(base_df[,2:ncol(base_df)]))
wp_dfb = data.frame(County = build_df[,"X"], ModWorkProds = round(rowSums(build_df[,2:ncol(build_df)])/BUILD_SAMPLE_RATE))
wp_df$ModWorkProds = wp_dfb$ModWorkProds[match(levels(wp_df$County)[wp_df$County], levels(wp_dfb$County)[wp_dfb$County])]

wp_df = wp_df[!wp_df$County == "Total",]
wp_df$County = levels(wp_df$County)[wp_df$County]

colnames(wp_df) = c("xvar", "yvar1", "yvar2")

p <- plotly_bar_plotter(data = wp_df, xlabel = "County", ylabel = "Tours", ynames = c(AO_CENSUS_LONG, BUILD_SCENARIO_NAME), percent = F, height = 275, tickangle = -320, bottom_offset = 25)
p

```

### {data-height=280}
```{r aWorkTourProdTable}

base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]

build_df = build_df[levels(build_df$X)[build_df$X] %in% levels(base_df$X)[base_df$X],colnames(build_df) %in% colnames(base_df)]

wp_df = data.frame(County = base_df[,"X"], ACSWorkProds = rowSums(base_df[,2:ncol(base_df)]))
wp_dfb = data.frame(County = build_df[,"X"], ModWorkProds = round(rowSums(build_df[,2:ncol(build_df)])/BUILD_SAMPLE_RATE))
wp_df$ModWorkProds = wp_dfb$ModWorkProds[match(levels(wp_df$County)[wp_df$County], levels(wp_dfb$County)[wp_dfb$County])]

wp_df = wp_df[!wp_df$County == "Total",]
wp_df$County = levels(wp_df$County)[wp_df$County]

colnames(wp_df) = c('County', 'ACS', 'ASim')

eval_expr <- paste("tworktourprod <- kable(wp_df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') ", sep = "")
eval(parse(text = eval_expr))
tworktourprod

```

Chart Column 1
--------------------------------------------

### Tour Attraction Comparison {data-height=300}
```{r aWorkTourAttrs}

base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]

build_df = build_df[levels(build_df$X)[build_df$X] %in% levels(base_df$X)[base_df$X],colnames(build_df) %in% colnames(base_df)]

wp_df = data.frame(County = base_df[,"X"], ACSWorkAttr = colSums(base_df[,2:ncol(base_df)]))
wp_dfb = data.frame(County = build_df[,"X"], ModWorkAttr = round(colSums(build_df[,2:ncol(build_df)])/BUILD_SAMPLE_RATE))
wp_df$ModWorkAttrs = wp_dfb$ModWorkAttr[match(levels(wp_df$County)[wp_df$County], levels(wp_dfb$County)[wp_dfb$County])]

wp_df = wp_df[!wp_df$County == "Total",]
wp_df$County = levels(wp_df$County)[wp_df$County]

colnames(wp_df) = c("xvar", "yvar1", "yvar2")

p <- plotly_bar_plotter(data = wp_df, xlabel = "County", ylabel = "Tours", ynames = c(AO_CENSUS_LONG, BUILD_SCENARIO_NAME), percent = F, height = 275, tickangle = -320, bottom_offset = 25)
p

```

### {data-height=280}

```{r aWorkTourAttrTable}
base_df <- base_data[[which(base_csv_names=="countyFlowsCensus")]]

build_pos <- which(build_csv_names=="countyFlows")
build_df <- build_data[[build_pos]]

build_df = build_df[levels(build_df$X)[build_df$X] %in% levels(base_df$X)[base_df$X],colnames(build_df) %in% colnames(base_df)]

wp_df = data.frame(County = base_df[,"X"], ACSWorkAttr = colSums(base_df[,2:ncol(base_df)]))
wp_dfb = data.frame(County = build_df[,"X"], ModWorkAttr = round(colSums(build_df[,2:ncol(build_df)])/BUILD_SAMPLE_RATE))
wp_df$ModWorkAttrs = wp_dfb$ModWorkAttr[match(levels(wp_df$County)[wp_df$County], levels(wp_dfb$County)[wp_dfb$County])]

wp_df = wp_df[!wp_df$County == "Total",]
wp_df$County = levels(wp_df$County)[wp_df$County]

colnames(wp_df) = c('County', 'ACS', 'ASim')

eval_expr <- paste("tworktourattr <- kable(wp_df, format = 'html', digits = 2, row.names = F, align = 'r', format.args = list(big.mark = ',')) %>%
  kable_styling('striped', font_size = 10, full_width=F, position='center') ", sep = "")
eval(parse(text = eval_expr))
tworktourattr

```



Zero Auto Households {data-navmenu="Long Term"}
============================================

Description {.sidebar data-width=175}
--------------------------------------------

********

**Census vs Model comparison at Census Tract level**

Compares number of zero auto households.

Census Data is from 2017 five year ACS.

Only for build scenario.

Summary
--------------------------------------------

### Zero Auto Households Census vs Model
```{r zero_auto_hh}

# BEWARE! shp data names are shortend by writeOGR in the script that creates the ct_zero_auto_shp file
#  Names in that script won't match names shown here.
#  For example, ct_zero_auto_shp@data$Diff_ZeroAuto ->  ct_zero_auto_shp@data$Dff_ZrA
labels <- sprintf(
  "%s
%s %.2f %s
%s", ct_zero_auto_shp@data$TRACTCE, ct_zero_auto_shp@data$txtCmm2, ct_zero_auto_shp@data$Dff_ZrA, "%", ct_zero_auto_shp@data$txtCmm1) %>% lapply(htmltools::HTML) bins <- c(-Inf, -100, -75, -50, -25, -5, 5, 25, 50, 75, 100, Inf) pal <- colorBin("PiYG", domain = ct_zero_auto_shp@data$Dff_ZrA, na.color="transparent", bins = bins) m <- leaflet(data = ct_zero_auto_shp)%>% addTiles() %>% addProviderTiles(providers$OpenStreetMap, group = "Background Map") %>% addLayersControl( overlayGroups = "Background Map", options = layersControlOptions(collapsed = FALSE) ) %>% addPolygons(group='ZeroCarDiff', fillColor = ~pal(Dff_ZrA), weight = 0.2, opacity = 1, color = "gray", stroke=T, dashArray = "5, 1", fillOpacity = 0.7, highlight = highlightOptions( weight = 1, color = "blue", dashArray = "", fillOpacity = 0.7, bringToFront = TRUE), label = labels, labelOptions = labelOptions( style = list("font-weight" = "normal", padding = "3px 8px"), textsize = "15px", direction = "auto")) %>% addLegend(pal = pal, values = ~density, opacity = 0.7, title = "Estimated(%) - Observed(%) Bins", position = "bottomright") m ``` Tour Summaries{data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=225} -------------------------------------------- This page summarizes day-pattern and tour generation model results. **Daily Activity Pattern** Results of Coordinated Daily Activity Pattern (CDAP) model, summarized for each person. _M_ : One or more mandatory tours _N_ : No mandatory tours but one or more non-mandatory tours _H_ : No tours (either home all day or out of area) **Percentage of Households with Joint Tour** Also the result of the CDAP model, summarized for each household. **Mandatory Tour Frequency** Result of the mandatory tour frequency model, summarized for each person with a daily activity pattern type _M_ **Tour rate by person type** Summary of tours per person resulting from all tour generation models. Joint tours are counted for each participant. **Individual non-mandatory tour frequency** Results of individual non-mandatory tour frequency model, summarized for each person with a daily activity pattern type _M_ or _N_. Chart Column 1 {data-width=160} -------------------------------------------- ### Daily Activity Pattern{data-height=500} ```{r Hist_DAP} base_df <- base_data[[which(base_csv_names=="dapSummary_vis")]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) base_df$DAP <- factor(base_df$DAP, levels = dap_types) build_df <- build_data[[which(build_csv_names=="dapSummary_vis")]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) build_df$DAP <- factor(build_df$DAP, levels = dap_types) base_df$grp <- BASE_SCENARIO_NAME build_df$grp <- BUILD_SCENARIO_NAME colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="DAP", y = c("freq"), grp = "PERNAME", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 250, xlabel = "DAP", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("pertype_dap", "Select Person Type", sd.pertype, ~grp_var,multiple=F)), p ) ``` ### Percentage of Households with a Joint Tour{data-height=300} ```{r Hist_Presence_Joint} base_pos <- which(base_csv_names=="hhsizeJoint") base_df <- base_data[[base_pos]] base_df <- base_df %>% group_by(HHSIZE) %>% mutate(percent = prop.table(freq)) %>% filter(JOINT==1) %>% ungroup() build_pos <- which(build_csv_names=="hhsizeJoint") build_df <- build_data[[build_pos]] build_df <- build_df %>% group_by(HHSIZE) %>% mutate(percent = prop.table(freq)) %>% filter(JOINT==1) %>% ungroup() colnames(build_df) <- colnames(base_df) std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "HHSIZE", y = c("percent")) p <- plotly_bar_plotter(data = std_DF, xlabel = "HH Size", ylabel = "Percent of Households", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = F, tickformat = ".0%", hoverformat = ".1%") p ``` ### Mandatory Tour Frequency{data-height=500} ```{r Hist_MTF} base_pos <- which(base_csv_names=="mtfSummary_vis") base_df <- base_data[[base_pos]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) base_df$mtf_name <- mtf_df$name[match(base_df$MTF, mtf_df$code)] base_df$mtf_name <- factor(base_df$mtf_name, levels = mtf_names) build_pos <- which(build_csv_names=="mtfSummary_vis") build_df <- build_data[[build_pos]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) build_df$mtf_name <- mtf_df$name[match(build_df$MTF, mtf_df$code)] build_df$mtf_name <- factor(build_df$mtf_name, levels = mtf_names) colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="mtf_name", y = c("freq"), grp = "PERNAME", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 250, xlabel = "MTF Choice", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickangle = -30, bottom_offset = 50) bscols(widths=c(3,9), list( filter_select("pertype_mtf", "Select Person Type", sd.pertype, ~grp_var,multiple=F)), p ) ``` Chart Column 1 {data-width=150} -------------------------------------------- ### Total Tour Rate (only active Persons) ```{r Hist_totaltours} base_df <- base_data[[which(base_csv_names=="total_tours_by_pertype_vis")]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) base_df1 <- base_data[[which(base_csv_names=="activePertypeDistbn")]] base_df$persons <- base_df1$freq[match(base_df$PERTYPE, base_df1$PERTYPE)] base_df$tourrate <- round(base_df$freq/base_df$persons,2) build_df <- build_data[[which(build_csv_names=="total_tours_by_pertype_vis")]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) build_df1 <- build_data[[which(build_csv_names=="activePertypeDistbn")]] build_df$persons <- build_df1$freq[match(build_df$PERTYPE, build_df1$PERTYPE)] build_df$tourrate <- round(build_df$freq/build_df$persons,2) colnames(build_df) <- colnames(base_df) std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "PERNAME", y = c("tourrate")) p <- plotly_bar_plotter(data = std_DF, xlabel = "Person Type", ylabel = "Tour Rate", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = F, height = 340, tickangle = -30, bottom_offset = 50) p ``` ### Persons by Individual Non-Mandatory Tours ```{r Hist_INM} base_df <- base_data[[which(base_csv_names=="inmSummary_vis")]] base_df$PERNAME <- person_type_df$name_char[match(base_df$PERTYPE, person_type_df$code)] base_df$PERNAME <- factor(base_df$PERNAME, levels = person_type_char) build_df <- build_data[[which(build_csv_names=="inmSummary_vis")]] build_df$PERNAME <- person_type_df$name_char[match(build_df$PERTYPE, person_type_df$code)] build_df$PERNAME <- factor(build_df$PERNAME, levels = person_type_char) colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nmtours", y = c("freq"), grp = "PERNAME", shared = T) #p <- plotly_bar_plotter(data = sd.pertype, height = 340, xlabel = "Number of Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, #BUILD_SCENARIO_NAME), percent = T, tickvals = c(seq(0,2), "3pl"), ticktext = c("0", "1", "2", "3pl")) p <- plotly_bar_plotter(data = sd.pertype, height = 340, xlabel = "Number of Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("pertype_mtf", "Select Person Type", sd.pertype, ~grp_var,multiple=F)), p ) ``` Joint Tours{data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=225} -------------------------------------------- ******** This page tabulates the results of the Joint Tour Frequency and Composition Model and the Joint Tour Person Participation Model. **Joint Tour Frequency** The frequency of households by number and purpose of joint tours. **Joint Tour Composition** The frequency of tours by composition (Adults only, Children only, Adults + Children). **Joint Tour Party Size** The frequency of joint tours by the number of household members participating in the tour. **Joint Tours by HH Size** The frequency of households by household size and the number of joint tours per household. **Joint Tours by HH Size** _Tour Level_ Distribution of joint tours by party size for each composition type. Chart Column 1 {data-width=150} -------------------------------------------- ### Joint Tour Frequency{data-height=675} ```{r jtf} base_df <- base_data[[which(base_csv_names=="jtf")]] build_df <- build_data[[which(build_csv_names=="jtf")]] # remove no joint tours option base_df <- base_df[-1,] build_df <- build_df[-1,] colnames(build_df) <- colnames(base_df) std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "alt_name", y = c("freq")) std_DF$xvar <- factor(std_DF$xvar, levels = jtf_alternatives) p <- plotly_bar_plotter(data = std_DF, xlabel = "Joint Tour Combination", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 500, bottom_offset = 275, tickangle = 300) p ``` ### Joint Tour Composition ```{r jtf_comp} base_df <- base_data[[which(base_csv_names=="jointComp")]] names(base_df)[names(base_df)=="tour_composition"] <- "COMPOSITION" build_df <- build_data[[which(build_csv_names=="jointComp")]] colnames(build_df) <- colnames(base_df) p1 <- plotly_pie_chart(data = base_df, label_var = "COMPOSITION", value_var = "freq", height = 250, title = BASE_SCENARIO_NAME, top_offset = 50) p2 <- plotly_pie_chart(data = build_df, label_var = "COMPOSITION", value_var = "freq", height = 250, title = BUILD_SCENARIO_NAME, top_offset = 50) bscols(widths=c(6,6), p1, p2 ) ``` Chart Column 1 {data-width=150} -------------------------------------------- ### Joint Tours By Number of Household Members ```{r jtf_partysize} base_df <- base_data[[which(base_csv_names=="jointPartySize")]] build_df <- build_data[[which(build_csv_names=="jointPartySize")]] colnames(build_df) <- colnames(base_df) build_df$freq[build_df$NUMBER_HH==5] <- sum(build_df$freq[build_df$NUMBER_HH>=5]) build_df <- build_df[build_df$NUMBER_HH<=5, ] std_DF <- get_standardDF(data_df1 = base_df, data_df2 = build_df, x = "NUMBER_HH", y = c("freq")) p <- plotly_bar_plotter(data = std_DF, xlabel = "Joint Party Size", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, height = 200) p ``` ### Joint Tours by Household Size ```{r jtf_byhhsize} base_pos <- which(base_csv_names=="jointToursHHSize") base_df <- base_data[[base_pos]] build_pos <- which(build_csv_names=="jointToursHHSize") build_df <- build_data[[build_pos]] colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="jointTours", y = c("freq"), grp = "hhsize", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 225, xlabel = "Number of Joint Tours", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("jtf_hhsize", "Select HH Size Group", sd.pertype, ~grp_var,multiple=F)), p ) ``` ### Party Size Distribution by Joint Tour Composition ```{r jtf_comppartysize} base_df <- base_data[[which(base_csv_names=="jointCompPartySize")]] build_df <- build_data[[which(build_csv_names=="jointCompPartySize")]] colnames(build_df) <- colnames(base_df) sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="partysize", y = c("freq"), grp = "comp", shared = T) p <- plotly_bar_plotter(data = sd.pertype, height = 225, xlabel = "Joint Party Size", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) bscols(widths=c(3,9), list( filter_select("jtf_comp", "Select Party Composition", sd.pertype, ~grp_var,multiple=F)), p ) ``` Destination{data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=225} -------------------------------------------- ******** **Non-Mandatory Tour Length Distribution** Results of non-mandatory tour destination choice models. Distribution of tours by distance between tour origin and destination for each non-mandatory tour purpose. Chart Column 1 {data-width=100} -------------------------------------------- ### Non-Mandatory Tour Length Distribution{data-height=350} ```{r nm_tlfd} base_df <- base_data[[which(base_csv_names=="tourDistProfile_vis")]] build_df <- build_data[[which(build_csv_names=="tourDistProfile_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$PURPOSE <- as.character(base_df$PURPOSE) build_df$PURPOSE <- as.character(build_df$PURPOSE) base_df$PURPOSE <- purpose_type_df$name[match(base_df$PURPOSE, purpose_type_df$code)] build_df$PURPOSE <- purpose_type_df$name[match(build_df$PURPOSE, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "distbin", y = c("freq"), grp = "PURPOSE") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Miles", percent = T, tickvals = seq(2,41), ticktext = c(seq(1,40), "40pl")) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), p1 ) ``` ### Average Non-Mandatory Tour Lengths (Miles){data-height=250} ```{r Table1_nonMandTripLength} base_df <- base_data[[which(base_csv_names=="nonMandTripLengths")]] build_df <- build_data[[which(build_csv_names=="nonMandTourLengths")]] df <- data.frame(base_df, build_df[,-1]) colnames(df) <- c("Purpose", BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME) df$Purpose <- purpose_type_df$name[match(df$Purpose, purpose_type_df$code)] t1 <- kable(df, format = "html", digits = 2, row.names = F, align = 'c', format.args = list(big.mark = ',')) %>% kable_styling("striped", full_width = F) t1 ``` TOD {data-navmenu="Tour Level"} ============================================ Description {.sidebar data-width=200} -------------------------------------------- ******** **Tour Departure Arrival & Duration** Tour Time-of-day Choice Model results. Each tour is assigned a time period of departure (time leaving home or work) and arrival (time arriving back at home or work). The entire day is divided into 18 one-hour bins (the first bin includes 3:00 AM to 6:00 AM and the last bin includes 11:00 PM to 3:00 AM). Tour duration is calculated as a function of departure and arrival period. It includes travel time and time spent at the primary destination and all intermediate stops. Results are shown for tours, filtered by tour purpose. ******** Chart Column 1 {.tabset} -------------------------------------------- ### Tour Departure-Arrival Profile ```{r tour_tod} base_df <- base_data[[which(base_csv_names=="todProfile_vis")]] base_df$tod_bin <- tod_df$bin[match(base_df$id, tod_df$id)] base_df$dur_bin <- dur_df$bin[match(base_df$id, dur_df$id)] build_df <- build_data[[which(build_csv_names=="todProfile_vis")]] build_df$tod_bin <- tod_df$bin[match(build_df$id, tod_df$id)] build_df$dur_bin <- dur_df$bin[match(build_df$id, dur_df$id)] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "id", y = c("freq_dep", "freq_arr", "freq_dur"), grp = "purpose") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Tour Departure", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 275) p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Tour Arrival", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 275) p3 <- plotly_density_plotter(sd.purpose, index = "three", xlabel = "Tour Duration", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = durBins, bottom_offset = 50, tickangle = 315, height = 225) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2, p3) ) ``` ### Tour Aggregate Departure-Arrival Profile ```{r tour_tod_agg} base_df <- base_data[[which(base_csv_names=="todProfile_vis")]] base_df$tod_agg <- cut(base_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) base_df$tod_agg <- factor(base_df$tod_agg, levels = timePeriodOrder) base_df <- base_df %>% dplyr::group_by(purpose, tod_agg) %>% dplyr::summarise(freq_dep = sum(freq_dep), freq_arr = sum(freq_arr), freq_dur = sum(freq_dur)) %>% dplyr::ungroup() base_df = base_df %>% arrange(match(tod_agg, timePeriodOrder)) build_df <- build_data[[which(build_csv_names=="todProfile_vis")]] build_df$tod_agg <- cut(build_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) build_df$tod_agg <- factor(build_df$tod_agg, levels = timePeriodOrder) build_df <- build_df %>% dplyr::group_by(purpose, tod_agg) %>% dplyr::summarise(freq_dep = sum(freq_dep), freq_arr = sum(freq_arr), freq_dur = sum(freq_dur)) %>% dplyr::ungroup() build_df = build_df %>% arrange(match(tod_agg, timePeriodOrder)) colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tod_agg", y = c("freq_dep", "freq_arr", "freq_dur"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Tour Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) p2 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Tour Arrival", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, index = 2) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2) ) ``` Tour Mode{data-navmenu="Tour Level"} ============================================ Chart Column 1{data-width=150} -------------------------------------------- ### Tour Mode Choice ```{r tourMode} # [3/31/2020] DH if(BUILD_SCENARIO_NAME == "CMAP_OBS") { # if running HTS vs OBS mode comparison base_df <- base_data[[which(base_csv_names=="tmodeProfile_vis_HTS")]] build_df <- build_data[[which(build_csv_names=="tmodeProfile_vis_calib")]] } else { base_df <- base_data[[which(base_csv_names=="tmodeProfile_vis_calib")]] build_df <- build_data[[which(build_csv_names=="tmodeProfile_vis")]] } build_df$purpose <- as.character(build_df$purpose) build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] colnames(build_df) <- colnames(base_df) base_df$purpose <- as.character(base_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] sd.pertype <- get_standardDF(data_df1=base_df, data_df2=build_df, x="id", y = c("freq_as0", "freq_as1", "freq_as2", "freq_all"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Zero Auto]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, bottom_offset = 55, tickangle = 300) p2 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Autos < Workers]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, index = 2, bottom_offset = 55, tickangle = 300) p3 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Autos >= Workers]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, index = 3, bottom_offset = 55, tickangle = 300) p4 <- plotly_bar_plotter(data = sd.pertype, height = 375, xlabel = "Tour Mode [Total]", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tourMode)), ticktext = tourMode, index = 4, bottom_offset = 55, tickangle = 300) filter_select("tourMode", "Select Tour Purpose", sd.pertype, ~grp_var,multiple=F) ``` ******** **Tour Mode Choice** Results of Tour Mode Choice Models, which selects a primary mode for each tour. Distribution of tours by tour mode and the ratio of autos to drivers in the household. Chart Column 2 {data-width=400} -------------------------------------------- ### ```{r tourMode2} bscols(widths=c(12), list(p1,p2) ) ``` Chart Column 3 {data-width=400} -------------------------------------------- ### ```{r tourMode3} bscols(widths=c(12), list(p3,p4) ) ``` Stop Frequency {data-navmenu="Trip Level"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Stop Frequency** Results of the Intermediate Stop Frequency Model, which predicts the number of intermediate stops on each tour by tour direction (outbound versus inbound). The summary shows percent of tours by number of stops on the tour and tour direction. **Stop Purpose** Results of the Intermediate Stop Purpose Model, which is currently implemented as a Monte Carlo choice according to probability distributions generated from survey data. The summary shows the percent of intermediate stops by stop purpose and tour purpose. Chart Column 1 {data-width=200} -------------------------------------------- ### Stop Frequency - Directional ```{r stopfreq_dir} base_df <- base_data[[which(base_csv_names=="stopfreqDir_vis")]] build_df <- build_data[[which(build_csv_names=="stopfreqDir_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.pertype1 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nstops", y = c("freq_out", "freq_inb"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype1, height = 325, xlabel = "Number of Stops - Outbound", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,4), ticktext = c("0", "1", "2", "3pl")) p2 <- plotly_bar_plotter(data = sd.pertype1, height = 325, xlabel = "Number of Stops - Inbound", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,4), ticktext = c("0", "1", "2", "3pl"), index = 2) bscols(widths=c(12), list( filter_select("stopfreq_dir", "Select Tour Purpose", sd.pertype1, ~grp_var,multiple=F), p1, p2) ) ``` Chart Column 1 {data-width=300} -------------------------------------------- ### Stop Frequency - Total{data-height=250} ```{r stopfreq_total} base_df <- base_data[[which(base_csv_names=="stopfreq_total_vis")]] build_df <- build_data[[which(build_csv_names=="stopfreq_total_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.pertype2 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="nstops", y = c("freq"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype2, height = 350, xlabel = "Number of Stops", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,7), ticktext = c("0", "1", "2", "3", "4", "5", "6pl")) bscols(widths=c(3,9), list( filter_select("stopfreq_total", "Select Tour Purpose", sd.pertype2, ~grp_var,multiple=F)), p1 ) ``` ### Stop Purpose by Tour Purpose{data-height=250} ```{r stoppurp_tourpurp} base_df <- base_data[[which(base_csv_names=="stoppurpose_tourpurpose_vis")]] build_df <- build_data[[which(build_csv_names=="stoppurpose_tourpurpose_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.pertype3 <- get_standardDF(data_df1=base_df, data_df2=build_df, x="stop_purp", y = c("freq"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.pertype3, height = 350, xlabel = "Stop Purpose", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,9), ticktext = stopPurposes[1:9]) bscols(widths=c(3,9), list( filter_select("stoppurp_tourpurp", "Select Tour Purpose", sd.pertype3, ~grp_var,multiple=F)), p1 ) ``` Location{data-navmenu="Trip Level"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Stop Location** Results of the Intermediate Stop Location Choice Model, which predicts the location of each intermediate stop. The summary shows the distribution of intermediate stops by out of direction distance and tour purpose. Out of direction distance is defined as the extra distance to the destination as a result of traveling through the stop location. For stops in the outbound direction, it is based on the distance between the last known location (the tour origin or previous outbound stop) and the tour primary destination. For stops in the inbound direction, it is based on the distance between the last known location (the tour primary destination or previous inbound stop) and the tour origin. Chart Column 1 {data-width=800} -------------------------------------------- ### Stop Location - Out of Direction Distance{data-height=350} ```{r stopDC} base_df <- base_data[[which(base_csv_names=="stopDC_vis")]] build_df <- build_data[[which(build_csv_names=="stopDC_vis")]] colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$PURPOSE <- as.character(base_df$PURPOSE) build_df$PURPOSE <- as.character(build_df$PURPOSE) base_df$PURPOSE <- purpose_type_df$name[match(base_df$PURPOSE, purpose_type_df$code)] build_df$PURPOSE <- purpose_type_df$name[match(build_df$PURPOSE, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "distbin", y = c("freq"), grp = "PURPOSE") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Out of Direction Distance (Miles)", percent = T, left_offset = 25, tickvals = seq(1,42), ticktext = outDirDist, height = 600, tickangle = 300, bottom_offset = 50) bscols(widths=c(12), list( filter_select("stopDC", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), p1) ) ``` Chart Column 1 {data-width=300} -------------------------------------------- ### Average Out of Direction Distance (Miles){data-height=250} ```{r Table1_outOfDir} base_df <- base_data[[which(base_csv_names=="avgStopOutofDirectionDist_vis")]] build_df <- build_data[[which(build_csv_names=="avgStopOutofDirectionDist_vis")]] df <- data.frame(base_df, build_df[,-1]) colnames(df) <- c("Tour_Purpose", BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME) df$Tour_Purpose <- purpose_type_df$name[match(df$Tour_Purpose, purpose_type_df$code)] # #t1 <- kable(df, format = "html", digits = 2, row.names = F, align = 'c', format.args = list(big.mark = ',')) %>% # kable_styling("striped", full_width = F) t1 <- htmlTable(txtRound(df, 2), align = "c|r", rnames = F, col.columns = c(rep("#E6E6F0", 1), rep("none", ncol(df) - 1)), caption = "_______________________________________________________") t1 ``` TOD{data-navmenu="Trip Level"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Stop Departure** Results of the Stop Departure Time Choice Model. The departure time of each stop on the tour is currently implemented as a Monte Carlo choice of time period from distributions generated from survey data. The entire day is divided into 18 one-hour bins (The first bin includes 3:00 AM to 6:00 AM and the last bin includes 11:00 PM to 3:00 AM). **Trip Departure** Summarizes all trips by departure time period, including trips to and from intermediate stops and the tour primary destination. Chart Column 1 {.tabset} -------------------------------------------- ### Stop & Trip Departure{data-height=650} ```{r stopDep} base_df <- base_data[[which(base_csv_names=="stopTripDep_vis")]] build_df <- build_data[[which(build_csv_names=="stopTripDep_vis")]] colnames(base_df) <- c("timebin", "purpose", "freq_stop", "freq_trip") colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_sharedData(data_df1 = base_df, data_df2 = build_df, run1_name = BASE_SCENARIO_NAME, run2_name = BUILD_SCENARIO_NAME, x = "timebin", y = c("freq_stop", "freq_trip"), grp = "purpose") p1 <- plotly_density_plotter(sd.purpose, index = "one", xlabel = "Stop Departure", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 400) p2 <- plotly_density_plotter(sd.purpose, index = "two", xlabel = "Trip Departure", percent = T, left_offset = 25, tickvals = seq(1,48), ticktext = todBins, bottom_offset = 150, tickangle = 315, height = 400) #p3 <- datatable(sd.purpose$data()) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2) ) ``` ### Aggregate Stop & Trip Departure ```{r trip_tod_agg} base_df <- base_data[[which(base_csv_names=="stopTripDep_vis")]] colnames(base_df) <- c("id","purpose","freq_stop","freq_trip") base_df$tod_agg <- cut(base_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) base_df$tod_agg <- factor(base_df$tod_agg, levels = timePeriodOrder) base_df <- base_df %>% group_by(purpose, tod_agg) %>% dplyr::summarise(freq_stop = sum(freq_stop), freq_trip = sum(freq_trip)) %>% ungroup() build_df <- build_data[[which(build_csv_names=="stopTripDep_vis")]] colnames(build_df) <- c("id","purpose","freq_stop","freq_trip") build_df$tod_agg <- cut(build_df$id, breaks = timePeriodBreaks, labels = timePeriods, right = FALSE) build_df$tod_agg <- factor(build_df$tod_agg, levels = timePeriodOrder) build_df <- build_df %>% group_by(purpose, tod_agg) %>% dplyr::summarise(freq_stop = sum(freq_stop), freq_trip = sum(freq_trip)) %>% ungroup() colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tod_agg", y = c("freq_stop", "freq_trip"), grp = "purpose", shared = T) p1 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Stop Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T) p2 <- plotly_bar_plotter(data = sd.purpose, height = 350, xlabel = "Trip Departure", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, index = 2) bscols(widths=c(2,10), filter_select("Tour Purpose", "Select Tour Purpose", sd.purpose, ~grp_var,multiple=F), list(p1, p2) ) ``` Trip Mode{data-navmenu="Trip Level"} ============================================ Chart Column 1 {data-width=125} -------------------------------------------- ### {data-height=200} ***Trip Mode Choice*** The results of the Trip Mode Choice Model, which predicts the mode of each trip on the tour. Distribution of trips by trip mode and tour mode, which constrains the availability of each trip mode and influences the utility of each available trip mode. ### Trip Mode Choice ```{r tripMode} if(BUILD_SCENARIO_NAME == 'CMAP_OBS') { base_df <- base_data[[which(base_csv_names=="tripModeProfile_vis_HTS")]] build_df <- build_data[[which(build_csv_names=="tripModeProfile_vis_calib")]] } else { build_df <- build_data[[which(build_csv_names=="tripModeProfile_vis")]] base_df <- base_data[[which(base_csv_names=="tripModeProfile_vis_calib")]] } colnames(build_df) <- colnames(base_df) # change purpose names to standard format base_df$purpose <- as.character(base_df$purpose) build_df$purpose <- as.character(build_df$purpose) base_df$purpose <- purpose_type_df$name[match(base_df$purpose, purpose_type_df$code)] build_df$purpose <- purpose_type_df$name[match(build_df$purpose, purpose_type_df$code)] sd.purpose <- get_standardDF(data_df1=base_df, data_df2=build_df, x="tripmode", y = c("value"), grp = "grp_var", shared = T) p <- plotly_bar_plotter(data = sd.purpose, height = 700, xlabel = "Trip Mode", ylabel = "Percent", ynames = c(BASE_SCENARIO_NAME, BUILD_SCENARIO_NAME), percent = T, tickvals = seq(1,length(tripMode)), ticktext = tripMode, bottom_offset = 75) bscols(widths=c(12), list(filter_select("tripMode1", "Select Tour Purpose", sd.purpose, ~purpose,multiple=F), filter_select("tripMode1", "Select Tour Mode", sd.purpose, ~tourmode,multiple=F)) ) ``` Chart Column 2 {data-width=800} -------------------------------------------- ### ```{r tripMode2} bscols(widths=c(12), list(p) ) ``` Count vs Volume: All Day{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### Count vs Volume - All Links{data-height=575} ```{r count_vol_dy} ggplot(hnet[hnet$DY_CNT > 0,], aes(x = DY_CNT, y = DY_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Daily Count", labels = comma) + scale_y_continuous("Daily Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_dy} ggplot(vgsum, aes(x = vgidx, y = prmse_DAILY, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_dy} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_DAILY, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_DAILY, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: NT{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### NT Count vs Volume - All Links{data-height=575} ```{r count_vol_nt} ggplot(hnet[hnet$NT_CNT > 0,], aes(x = NT_CNT, y = NT_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Night Count", labels = comma) + scale_y_continuous("Night Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_nt} ggplot(vgsum, aes(x = vgidx, y = prmse_NT, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_nt} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_NT, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_NT, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: EA{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### EA Count vs Volume - All Links{data-height=575} ```{r count_vol_ea} ggplot(hnet[hnet$EA_CNT > 0,], aes(x = EA_CNT, y = EA_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Early Morning Count", labels = comma) + scale_y_continuous("Early Morning Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_ea} ggplot(vgsum, aes(x = vgidx, y = prmse_EA, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_ea} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_EA, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_EA, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: AM{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### AM Count vs Volume - All Links{data-height=575} ```{r couAM_vol_am} ggplot(hnet[hnet$AM_CNT > 0,], aes(x = AM_CNT, y = AM_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("AM Peak Count", labels = comma) + scale_y_continuous("AM Peak Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_am} ggplot(vgsum, aes(x = vgidx, y = prmse_AM, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_am} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_AM, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_AM, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: MM{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### MM Count vs Volume - All Links{data-height=575} ```{r count_vol_mm} ggplot(hnet[hnet$MM_CNT > 0,], aes(x = MM_CNT, y = MM_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Mid Morning Count", labels = comma) + scale_y_continuous("Mid Morning Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_mm} ggplot(vgsum, aes(x = vgidx, y = prmse_MM, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_mm} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_MM, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_MM, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: MD{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### MD Count vs Volume - All Links{data-height=575} ```{r count_vol_md} ggplot(hnet[hnet$MD_CNT > 0,], aes(x = MD_CNT, y = MD_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Midday Count", labels = comma) + scale_y_continuous("Midday Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_md} ggplot(vgsum, aes(x = vgidx, y = prmse_MD, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_md} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_MD, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_MD, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: AF{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### AF Count vs Volume - All Links{data-height=575} ```{r count_vol_af} ggplot(hnet[hnet$AF_CNT > 0,], aes(x = AF_CNT, y = AF_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Afternoon Count", labels = comma) + scale_y_continuous("Afternoon Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_af} ggplot(vgsum, aes(x = vgidx, y = prmse_AF, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_af} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_AF, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_AF, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: PM{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### PM Count vs Volume - All Links{data-height=575} ```{r count_vol_pm} ggplot(hnet[hnet$PM_CNT > 0,], aes(x = PM_CNT, y = PM_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("PM Peak Count", labels = comma) + scale_y_continuous("PM Peak Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_pm} ggplot(vgsum, aes(x = vgidx, y = prmse_PM, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_pm} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_PM, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_PM, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ``` Count vs Volume: EV{data-navmenu="Assignment"} ============================================ Description {.sidebar data-width=175} -------------------------------------------- ******** **Link level count comparison** Results of auto assignment. Comparison of observed counts and assigned volumes on each link with a counted volume, by assignment time period. Chart Column 2{.tabset} -------------------------------------------- ### EV Count vs Volume - All Links{data-height=575} ```{r count_vol_ev} ggplot(hnet[hnet$EV_CNT > 0,], aes(x = EV_CNT, y = EV_ASN)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + scale_x_continuous("Evening Count", labels = comma) + scale_y_continuous("Evening Assignment", labels = comma) ``` ### RMSE Statistics{data-height=575} ```{r rmse_vol_ev} ggplot(vgsum, aes(x = vgidx, y = prmse_EV, color = "Model")) + geom_line(stat = "identity", size = 1.2) + geom_line(aes(y = limit/100, color = "Limit"), stat = "identity", size = 1.2) + scale_y_continuous("Percent RMSE", labels = percent) + scale_x_continuous("Volume Group", labels = function(x){return(vgsum[x + 1,"vg"])}, breaks = 0:nrow(vgsum)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` ### Assigned VMT Statistics{data-height=575} ```{r vmtcomp_ev} df = rbind.data.frame(cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$asnvmt_EV, "source" = "Model"), cbind("FTYPE" = vmtcomp$FTYPE, "VMT" = vmtcomp$obsvmt_EV, "source" = "Observed"), stringsAsFactors = FALSE) df$VMT = as.numeric(df$VMT) ggplot(df, aes(x = FTYPE, y = VMT, fill = source)) + geom_bar(stat = "identity", position = "dodge") + scale_x_discrete("") + scale_color_discrete("Source") + scale_y_continuous("Vehicle Miles of Travel", labels = comma) ```